Ridge flow models for image segmentation

In this paper we introduce a new algorithm for segmentation of medical images of any dimension. The segmentation is based on geometric methods and multiscale analysis. A sequence of increasingly blurred images is created by Gaussian blurring. Each blurred image is segmented by locating its ridges, decomposing the ridges into curvilinear segments and assigning a unique label to each, and constructing a region for each ridge segment based on a flow model which uses vector fields naturally associated with the ridge finding. The regions from the initial image are leaf nodes in a tree. The regions from the blurred images are interior nodes of the tree. Arcs of the tree are constructed based on how regions at one scale merge via blurring into regions at the next scale. Objects in the image are represented by unions and differences of subtrees of the full tree. The tree is used as input to a visualization program which allows the user to interactively explore the hierarchy and define objects. Some results are provided for a 3D magnetic resonance image of a head.

[1]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[2]  S Grossberg,et al.  Neural dynamics of brightness perception: Features, boundaries, diffusion, and resonance , 1984, Perception & Psychophysics.

[3]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  John M. Gauch,et al.  Image Description Via The Multiresolution Intensity Axis Of Symmetry , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[5]  Glynn P. Robinson,et al.  Scale and segmentation of grey-level images using maximum gradient paths , 1991, Image Vis. Comput..

[6]  David H. Eberly,et al.  The magic crayon: an object definition and volume calculation testbed , 1993 .

[7]  Stephen M. Pizer,et al.  Toward Interactive Object Definition in 3D Scalar Images , 1990 .

[8]  James M. Coggins,et al.  Interactive object definition in medical images using multiscale, geometric image descriptions , 1990, [1990] Proceedings of the First Conference on Visualization in Biomedical Computing.

[9]  Stephen Grossberg,et al.  Neural dynamics of brightness perception: Features, boundaries, diffusion, and resonance , 1984 .

[10]  John M. Gauch,et al.  Multiresolution Shape Descriptions and their Applications in Medical Imaging , 1988 .

[11]  David H. Eberly,et al.  Interacting with image hierarchies for fast and accurate object segmentation , 1994, Medical Imaging.

[12]  R. Whitaker.  Geometry-limited diffusion in the characterization of geometric patches in images , 1993 .

[13]  Azriel Rosenfeld,et al.  Segmentation and Estimation of Image Region Properties through Cooperative Hierarchial Computation , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  James L. Crowley,et al.  A Representation for Shape Based on Peaks and Ridges in the Difference of Low-Pass Transform , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Morris Goldberg,et al.  Hierarchy in Picture Segmentation: A Stepwise Optimization Approach , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  David H. Eberly,et al.  The WMMR filters: a class of robust edge enhancers , 1993, IEEE Trans. Signal Process..

[17]  Stephen M. Pizer,et al.  A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema , 1990, IEEE Trans. Pattern Anal. Mach. Intell..